Privacy-preserving Collaborative Filtering Using Randomized Response

Abstract

This paper proposes a new privacy-preserving recommendation method classified into a randomized perturbation scheme in which a user adds random noise to the original rating value and a server provides a disguised data to allow users to predict rating value for unseen items. The proposed scheme performs perturbation in {\em randomized response} scheme, which preserves higher degree of privacy than that of additive perturbation. To address the accuracy reduction of the randomized response, the proposed scheme uses a {\em posterior probability distribution function}, derived from Bayes' estimation to reconstruction of the original distribution, to revise the similarity between items computed from the disguised matrix. A simple experiment shows the accuracy improvement of the proposed scheme.